Text cleaning pipeline in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert all text in the list to lowercase.

Data Analysis Python

texts = ['Hello World', 'Data Science']
lower_texts = [text.[1]() for text in texts]
print(lower_texts)

Drag options to blanks, or click blank then click option'

Aupper

Blower

Ctitle

Dcapitalize

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'upper()' instead of 'lower()' changes text to uppercase.

Using 'capitalize()' only changes the first letter.

The lower() method converts all characters in a string to lowercase.

2fill in blank

medium

Complete the code to remove leading and trailing spaces from each string.

Data Analysis Python

texts = ['  data ', ' science  ']
clean_texts = [text.[1]() for text in texts]
print(clean_texts)

Drag options to blanks, or click blank then click option'

Ajoin

Bsplit

Creplace

Dstrip

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'split()' splits the string into words, not removing spaces.

Using 'replace()' needs extra arguments to remove spaces.

The strip() method removes spaces from the start and end of a string.

3fill in blank

hard

Fix the error in the code to remove punctuation from the text using string.punctuation.

Data Analysis Python

import string
text = 'Hello, world!'
clean_text = ''.join(char for char in text if char not in [1])
print(clean_text)

Drag options to blanks, or click blank then click option'

Astring.punct

Bstring.punctuations

Cstring.punctuation

Dpunctuation.string

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'string.punct' which does not exist.

Swapping the order like 'punctuation.string' which is invalid.

The correct attribute for all punctuation characters is string.punctuation.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Data Analysis Python

words = ['data', 'is', 'fun', 'science']
lengths = {word: [1] for word in words if [2]
print(lengths)

Drag options to blanks, or click blank then click option'

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Comparing the word string directly to a number.

The dictionary comprehension maps each word to its length using len(word). The condition keeps words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their lengths for words longer than 4 characters.

Data Analysis Python

words = ['hello', 'data', 'science', 'fun']
result = { [1]: [2] for w in words if [3] }
print(result)

Drag options to blanks, or click blank then click option'

Aw.upper()

Blen(w)

Clen(w) > 4

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys.

Not filtering words by length.

The dictionary comprehension uses w.upper() as keys, len(w) as values, and filters words with length greater than 4.

Practice

1. What is the main purpose of a text cleaning pipeline in data analysis?

easy

A. To translate text into another language

B. To make text uniform and easier to analyze

C. To increase the length of the text

D. To add more punctuation to the text

Solution

1. Step 1: Understand text cleaning goal

   Text cleaning aims to prepare text data by making it consistent and easier to work with.

2. Step 2: Identify correct purpose

   Adding punctuation or increasing length does not help analysis; uniformity does.

3. Final Answer:

   To make text uniform and easier to analyze -> Option B

4. Quick Check:

   Text cleaning = uniform text [OK]

Hint: Text cleaning makes text consistent for analysis [OK]

Common Mistakes:

• Thinking cleaning adds punctuation
• Confusing cleaning with translation
• Believing cleaning increases text length

2. Which of the following Python code snippets correctly converts a string text to lowercase?

easy

A. text.lower()

B. lower(text)

C. text.toLowerCase()

D. text.lowercase()

Solution

1. Step 1: Recall Python string method for lowercase

   Python strings have a method called lower() to convert text to lowercase.

2. Step 2: Check each option

   text.lower() uses correct syntax. Options B, C, D are invalid in Python.

3. Final Answer:

   text.lower() -> Option A

4. Quick Check:

   Python lowercase method = lower() [OK]

Hint: Use text.lower() to lowercase strings in Python [OK]

Common Mistakes:

• Using JavaScript method toLowerCase() in Python
• Trying a non-existent function lower() outside string
• Using incorrect method name like lowercase()

3. Given the following Python code, what is the output of cleaned_text?

import string
text = " Hello, World!  "
cleaned_text = text.lower().strip().translate(str.maketrans('', '', string.punctuation))
print(cleaned_text)

medium

A. " hello world "

B. "hello, world!"

C. "hello world"

D. "HELLO WORLD"

Solution

1. Step 1: Apply lower() and strip()

   The text becomes "hello, world!" after lowering and removing spaces at ends.

2. Step 2: Remove punctuation with translate()

   All punctuation marks like comma and exclamation are removed, resulting in "hello world".

3. Final Answer:

   "hello world" -> Option C

4. Quick Check:

   Lowercase + strip + remove punctuation = "hello world" [OK]

Hint: Chain methods: lower, strip, then remove punctuation [OK]

Common Mistakes:

• Forgetting to strip spaces
• Not removing punctuation correctly
• Confusing case conversion order

4. Identify the error in this text cleaning function and choose the fix:

def clean_text(text):
    text = text.lower()
    text = text.strip
    return text

print(clean_text("  DATA Science  "))

medium

A. Change text.strip to text.strip()

B. Change text.lower() to lower(text)

C. Add text = text.replace() to remove spaces

D. Remove the return statement

Solution

1. Step 1: Check method usage

   strip is a method and must be called with parentheses ().

2. Step 2: Identify fix

   Changing text.strip to text.strip() correctly removes spaces.

3. Final Answer:

   Change text.strip to text.strip() -> Option A

4. Quick Check:

   Method calls need parentheses [OK]

Hint: Always call string methods with parentheses [OK]

Common Mistakes:

• Forgetting parentheses on methods
• Using wrong syntax for lower()
• Removing return statement by mistake

5. You have a list of sentences with inconsistent spacing and punctuation:

texts = ["Data Science!  ", "  Machine Learning.", "AI, is fun!"]

Which code snippet correctly cleans all texts by making them lowercase, removing punctuation, and trimming spaces?

hard

A. cleaned = [t.strip().lower() for t in texts]

B. cleaned = [t.lower().replace(' ', '') for t in texts]

C. cleaned = [t.translate(str.maketrans('', '', string.punctuation)) for t in texts]

D. import string
cleaned = [t.lower().strip().translate(str.maketrans('', '', string.punctuation)) for t in texts]

Solution

1. Step 1: Understand cleaning steps needed

   We need lowercase, remove punctuation, and trim spaces for each text.

2. Step 2: Check each option

   import string cleaned = [t.lower().strip().translate(str.maketrans('', '', string.punctuation)) for t in texts] applies all three steps correctly using list comprehension. Others miss one or more steps.

3. Final Answer:

   import string cleaned = [t.lower().strip().translate(str.maketrans('', '', string.punctuation)) for t in texts] -> Option D

4. Quick Check:

   All steps in list comprehension = import string cleaned = [t.lower().strip().translate(str.maketrans('', '', string.punctuation)) for t in texts] [OK]

Hint: Use list comprehension chaining all cleaning steps [OK]

Common Mistakes:

• Missing punctuation removal
• Not trimming spaces
• Removing spaces incorrectly with replace